heliqi commented on code in PR #14172:
URL: https://github.com/apache/tvm/pull/14172#discussion_r1132013613
##########
python/tvm/relay/frontend/paddlepaddle.py:
##########
@@ -400,6 +400,30 @@ def convert_conv2d_transpose(g, op, block):
g.add_node(op.output("Output")[0], out)
+def convert_dist(g, op, block):
+ """Operator converter for dist."""
+
+ x = g.get_node(op.input("X")[0])
+ y = g.get_node(op.input("Y")[0])
+ z = _op.abs(_op.subtract(x, y))
Review Comment:
z = x - y. It can't use `_op.abs`, otherwise `inv_p=_expr.const(1.0 / p,
dtype=dtype)` miscalculated.
##########
python/tvm/relay/frontend/paddlepaddle.py:
##########
@@ -475,6 +499,50 @@ def convert_elementwise_op(g, op, block):
g.add_node(op.output("Out")[0], out)
+def convert_linspace(g, op, block):
+ """Operator converter for linspace."""
+
+ start = g.get_node(op.input("Start")[0])
+ stop = g.get_node(op.input("Stop")[0])
+ num = g.get_node(op.input("Num")[0])
+ dtype = _convert_dtype_value(op.attr("dtype"))
+ start, infered = try_infer_value(start, parameters=g.get_params())
+ if infered:
+ start = start.tolist()[0]
+ else:
+ msg = 'Value {} in attribute "start" of operator Linspace is not
"valid."'
+ raise tvm.error.OpAttributeInvalid(msg.format(start))
+
+ stop, infered = try_infer_value(stop, parameters=g.get_params())
+ if infered:
+ stop = stop.tolist()[0]
+ else:
+ msg = 'Value {} in attribute "stop" of operator Linspace is not
"valid."'
+ raise tvm.error.OpAttributeInvalid(msg.format(stop))
+
+ num, infered = try_infer_value(num, parameters=g.get_params())
+ if infered:
+ num = num.tolist()[0]
+ else:
+ msg = 'Value {} in attribute "num" of operator Linspace is not
"valid."'
+ raise tvm.error.OpAttributeInvalid(msg.format(num))
+
+ if num == 1:
+ out = _op.full(_expr.const(start, dtype), shape=(1))
+ else:
+ if dtype in ["int32", "int64"]:
Review Comment:
dtype support float32, float64, int32 and int64
##########
python/tvm/relay/frontend/paddlepaddle.py:
##########
@@ -475,6 +499,50 @@ def convert_elementwise_op(g, op, block):
g.add_node(op.output("Out")[0], out)
+def convert_linspace(g, op, block):
+ """Operator converter for linspace."""
+
+ start = g.get_node(op.input("Start")[0])
+ stop = g.get_node(op.input("Stop")[0])
+ num = g.get_node(op.input("Num")[0])
+ dtype = _convert_dtype_value(op.attr("dtype"))
+ start, infered = try_infer_value(start, parameters=g.get_params())
Review Comment:
Dynamic shape should also be supported
##########
python/tvm/relay/frontend/paddlepaddle.py:
##########
@@ -514,6 +582,27 @@ def convert_expand_as(g, op, block):
g.add_node(op.output("Out")[0], out)
+def convert_eye(g, op, block):
+ """Operator converter for eye."""
+
+ num_rows = op.attr("num_rows")
+ num_columns = op.attr("num_columns")
Review Comment:
num_columns might equal -1, in which case num_columns equals num_rows.
##########
tests/python/frontend/paddlepaddle/test_forward.py:
##########
@@ -1969,5 +1969,142 @@ def forward(self, inputs):
verify_model(Mish(), input_data=input_data)
[email protected]_gpu
+def test_forward_thresholded_relu():
+ class ThresholdedRelu(nn.Layer):
+ @paddle.jit.to_static
+ def forward(self, inputs):
+ return nn.functional.thresholded_relu(inputs)
+
+ input_shapes = [[10], [2, 3], [5, 10, 11], [3, 4, 5, 6]]
+ for input_shape in input_shapes:
+ input_data = paddle.randn(shape=input_shape, dtype="float32")
+ verify_model(ThresholdedRelu(), input_data=input_data)
+
+
[email protected]_gpu
+def test_forward_index_select():
+ class IndexSelect1(nn.Layer):
+ @paddle.jit.to_static
+ def forward(self, x, index):
+ return paddle.index_select(x, index, axis=0)
+
+ class IndexSelect2(nn.Layer):
+ @paddle.jit.to_static
+ def forward(self, x, index):
+ return paddle.index_select(x, index, axis=-1)
+
+ input_shapes = [[10], [2, 3], [5, 10, 11], [3, 4, 5, 6]]
+ for input_shape in input_shapes:
+ input_data = paddle.randn(shape=input_shape, dtype="float32")
+ index = paddle.to_tensor([0, 1, 1], dtype="int32")
+ verify_model(IndexSelect1(), input_data=[input_data, index])
+ verify_model(IndexSelect2(), input_data=[input_data, index])
+
+
[email protected]_gpu
+def test_forward_eye():
+ class Eye1(nn.Layer):
+ @paddle.jit.to_static
+ def forward(self, inputs):
+ return paddle.eye(3, 5, dtype="int32"), paddle.eye(3, 5,
dtype="float32"), inputs
+
+ class Eye2(nn.Layer):
Review Comment:
Add case where num_columns is None
##########
python/tvm/relay/frontend/paddlepaddle.py:
##########
@@ -475,6 +499,50 @@ def convert_elementwise_op(g, op, block):
g.add_node(op.output("Out")[0], out)
+def convert_linspace(g, op, block):
+ """Operator converter for linspace."""
+
+ start = g.get_node(op.input("Start")[0])
+ stop = g.get_node(op.input("Stop")[0])
+ num = g.get_node(op.input("Num")[0])
+ dtype = _convert_dtype_value(op.attr("dtype"))
+ start, infered = try_infer_value(start, parameters=g.get_params())
+ if infered:
+ start = start.tolist()[0]
+ else:
+ msg = 'Value {} in attribute "start" of operator Linspace is not
"valid."'
+ raise tvm.error.OpAttributeInvalid(msg.format(start))
+
+ stop, infered = try_infer_value(stop, parameters=g.get_params())
+ if infered:
+ stop = stop.tolist()[0]
+ else:
+ msg = 'Value {} in attribute "stop" of operator Linspace is not
"valid."'
+ raise tvm.error.OpAttributeInvalid(msg.format(stop))
+
+ num, infered = try_infer_value(num, parameters=g.get_params())
+ if infered:
+ num = num.tolist()[0]
+ else:
+ msg = 'Value {} in attribute "num" of operator Linspace is not
"valid."'
+ raise tvm.error.OpAttributeInvalid(msg.format(num))
+
+ if num == 1:
+ out = _op.full(_expr.const(start, dtype), shape=(1))
+ else:
+ if dtype in ["int32", "int64"]:
+ start = int(start)
+ stop = int(stop)
+ step = (stop - start) / (num - 1)
+ stop = stop + step
+ start = _expr.const(start, "float32")
+ stop = _expr.const(stop, "float32")
+ step = _expr.const(step, "float32")
+ out = _op.transform.arange(start=start, stop=stop, step=step,
dtype="float32")
Review Comment:
Why is dtype fixed to "float32"?
##########
tests/python/frontend/paddlepaddle/test_forward.py:
##########
@@ -1969,5 +1969,142 @@ def forward(self, inputs):
verify_model(Mish(), input_data=input_data)
[email protected]_gpu
+def test_forward_thresholded_relu():
+ class ThresholdedRelu(nn.Layer):
+ @paddle.jit.to_static
+ def forward(self, inputs):
+ return nn.functional.thresholded_relu(inputs)
Review Comment:
Add some cases where the threshold is 0.5, paddle.randn ranges from 0 to 1
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